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Swarm-based multi-agent simulation leads to better modeling of tasks in biology, engineering, economics, art, and many other areas. It also facilitates an understanding of complicated phenomena that cannot be solved analytically. Agent-Based Modeling and Simulation with Swarm provides the methodology for a multi-agent-based modeling approach that integrates computational techniques such as artificial life, cellular automata, and bio-inspired optimization. Each chapter gives an overview of the problem, explores state-of-the-art technology in the field, and discusses multi-agent frameworks. The author describes step by step how to assemble algorithms for generating a simulation model, program, method for visualization, and further research tasks. While the book employs the commonly used Swarm system, readers can model and develop the simulations with their own simulator. To encourage hands-on exploration of emergent systems, Swarm-based software and source codes are available for download from the author’s website. A thorough overview of multi-agent simulation and supporting tools, this book shows how this type of simulation is used to acquire an understanding of complex systems and artificial life. It carefully explains how to construct a simulation program for various applications.
Swarm-based multi-agent simulation leads to better modeling of tasks in biology, engineering, economics, art, and many other areas. It also facilitates an understanding of complicated phenomena that cannot be solved analytically. Agent-Based Modeling and Simulation with Swarm provides the methodology for a multi-agent-based modeling approach that i
Agent-based modeling and simulation (ABMS), a way to simulate a large number of choices by individual actors, is one of the most exciting practical developments in business modeling since the invention of relational databases. It represents a new way to understand data and generate information that has never been available before--a way for businesses to view the future and to understand and anticipate the likely effects of their decisions on their markets and industries. It thus promises to have far-reaching effects on the way that businesses in many areas use computers to support practical decision-making. Managing Business Complexity is the first complete business-oriented agent-based modeling and simulation resource. It has three purposes: first, to teach readers how to think about ABMS, that is, about agents and their interactions; second, to teach readers how to explain the features and advantages of ABMS to other people and third, to teach readers how to actually implement ABMS by building agent-based simulations. It is intended to be a complete ABMS resource, accessible to readers who haven't had any previous experience in building agent-based simulations, or any other kinds of models, for that matter. It is also a collection of ABMS business applications resources, all assembled in one place for the first time. In short, Managing Business Complexity addresses who needs ABMS and why, where and when ABMS can be applied to the everyday business problems that surround us, and how specifically to build these powerful agent-based models.
"Swarm, a standard set of program libraries, allows users to construct simulations where a collection of heterogeneous independent agents or elements interact through discrete events. This volume offers the first extensive tutorial to the use of these software libraries developed at the Santa Fe Institute as part of the ongoing research into complexity."--BOOK JACKET.
A comprehensive and hands-on introduction to the core concepts, methods, and applications of agent-based modeling, including detailed NetLogo examples. The advent of widespread fast computing has enabled us to work on more complex problems and to build and analyze more complex models. This book provides an introduction to one of the primary methodologies for research in this new field of knowledge. Agent-based modeling (ABM) offers a new way of doing science: by conducting computer-based experiments. ABM is applicable to complex systems embedded in natural, social, and engineered contexts, across domains that range from engineering to ecology. An Introduction to Agent-Based Modeling offers a comprehensive description of the core concepts, methods, and applications of ABM. Its hands-on approach—with hundreds of examples and exercises using NetLogo—enables readers to begin constructing models immediately, regardless of experience or discipline. The book first describes the nature and rationale of agent-based modeling, then presents the methodology for designing and building ABMs, and finally discusses how to utilize ABMs to answer complex questions. Features in each chapter include step-by-step guides to developing models in the main text; text boxes with additional information and concepts; end-of-chapter explorations; and references and lists of relevant reading. There is also an accompanying website with all the models and code.
Operational Research (OR) deals with the use of advanced analytical methods to support better decision-making. It is multidisciplinary with strong links to management science, decision science, computer science and many application areas such as engineering, manufacturing, commerce and healthcare. In the study of emergent behaviour in complex adaptive systems, Agent-based Modelling & Simulation (ABMS) is being used in many different domains such as healthcare, energy, evacuation, commerce, manufacturing and defense. This collection of articles presents a convenient introduction to ABMS with papers ranging from contemporary views to representative case studies. The OR Essentials series presents a unique cross-section of high quality research work fundamental to understanding contemporary issues and research across a range of Operational Research (OR) topics. It brings together some of the best research papers from the esteemed Operational Research Society and its associated journals, also published by Palgrave Macmillan.
Although there are plenty of publications dealing with the theory of multi-agent systems and agent-based simulations, information about the practical development of such systems is scarce. The aim of this book is to fill this empty space and to provide knowledge about design and development of agent-based simulations in an easy and comprehensible way. The book begins with the fundamentals of multi-agent systems, agent principles and their interaction, and goes on to discuss the philosophy of agent-based programming. Agent-based models - like any other scientific method - have drawbacks and limitations, which are presented in the book as well. The main portion of the text is then devoted to a description of methodology and best practices for the design and development of agent-based simulation software. The methodology (called Agentology) guides the reader through the entire development process, from the formal definition of the problem, through conceptual modeling and the selection of the particular development platform, to the programming and debugging of the code itself and the final assessment of the model. The visual language as the means of representation of the conceptual model is included. The reader is also presented with a comparison of present multi-agent development environments and tools, which could be helpful for the selection of appropriate development instruments. Given that the theoretical foundation is presented in an accessible way and supported by many practical examples, figures, schemes and source codes, this publication is especially suitable as a textbook for introductory graduate-level courses on multi-agent systems and agent-based modeling. Besides appealing to students and the scientific community, the monograph can aid software architects and developers who are not familiar with agent principles, conveying valuable insights into this distinct computer paradigm.
This volume on financial and economic simulations in Swarmmarks the continued progress by a group of researchers to incorporateagent-based computer models as an important tool within theirdiscipline.Swarm promotes agent-based computer models as a tool for the study ofcomplex systems. A common "language" is leading to the growth ofuser communities in specific areas of application. Furthermore, byproviding an organizing framework to guide the development of moreproblem-specific structures, and by dealing with a whole range ofissues that affect their fundamental correctness and their ability tobe developed and reused, Swarm has sought to make the use ofagent-based models a legitimate tool of scientific investigation thatalso meets the practical needs of investigators within acommunity.Swarm's principal foundation is an object-oriented representation ofactive agents interacting among themselves and with their environment.To this base layer it adds its own structures to drive, record andportrait the events that occur across this world. The specificcontents of any world, however, are up to the experimenter to provide, either by building them from scratch or by tapping previouscontributions.This book is notable in assembling a rich array of such contributions, which are significant in their own right, but which can also be minedto extract the reusable elements in their respective areas of financeand economics. It also presents three interesting software additionswith tutorials in the form of simple financial and economicapplications. A Swarm meta-language closer to a natural language', the use of internet-augmented Swarm for experimental economics, and aSwarm visual builder will meet thechallenges launched by otheragent-based modelling competitors.The Swarm community at large can benefit greatly from the lead thatthe growing field of computational economics is taking to address itsown needs, as represented by th
Agent-based models are tools that provide researchers in economic fields with unprecedented analytical capabilities. This book describes the power of agent-based models along their methodology, and it provides several examples of applications spanning from public policy evaluation to financial markets.
Computer simulations of economic systems are slowly gaining ground within the economic profession. However, such a process is hindered by a lack of communication among researchers who do not share a common language. For its object-oriented structure and its versatility, Swarm has the necessary characteristics to become a credible universal language of agent-based simulations. Economic Simulations in Swarm collects a series of original articles in such domains as macro and micro economics, industrial organization, monetary theory, and finance, all linked by a common denominator: the use of the Swarm simulation platform. Swarm, a standard set of program libraries, allows users to construct simulations where a collection of heterogeneous independent agents or elements interact through discrete events. This volume offers the first extensive tutorial to the use of these software libraries developed at the Santa Fe Institute as part of the ongoing research into complexity. The editors conceived the idea of this book while visiting the Santa Fe Institute as members of the `Working Group on Adaptive and Computable Economics'. Francesco Luna is a specialist in Computable Economics, and Benedikt Stefansson is an active contributor to the Swarm community.